LAR-SR: A Local Autoregressive Model for Image Super-Resolution
Previous super-resolution (SR) approaches often formulate SR as a regression problem and pixel wise restoration, which leads to a blurry and unreal SR output. Recent works combine adversarial loss with pixel-wise loss to train a GAN-based model or introduce normalizing flows into SR problems to generate more realistic images. As another powerful generative approach, autoregressive (AR) model has not been noticed in low level tasks due to its limitation. Based on the fact that given the structural information, the textural details in the natural images are locally related without long term dependency, in this paper we propose a novel autoregressive model-based SR approach, namely LAR-SR, which can efficiently generate realistic SR images using a novel local autoregressive (LAR) module. The proposed LAR module can sample all the patches of textural components in parallel, which greatly reduces the time consumption. In addition to high time efficiency, it is also able to leverage contextual information of pixels and can be optimized with a consistent loss. Experimental results on the widely-used datasets show that the proposed LAR-SR approach achieves superior performance on the visual quality and quantitative metrics compared with other generative models such as GAN, Flow, and is competitive with the mixture generative model.
Code (1)
Tasks
Image Super-ResolutionSuper-ResolutionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis
In this paper, we present NUWA-Infinity, a generative model for infinite visual synthesis, which is defined as the task of generating arbitrarily-sized high-resolution images or long-duration videos. An autoregressive ov…
Image OutpaintingText-to-Image GenerationVideo GenerationPerceive, Understand and Restore: Real-World Image Super-Resolution with Autoregressive Multimodal Generative Models
By leveraging the generative priors from pre-trained text-to-image diffusion models, significant progress has been made in real-world image super-resolution (Real-ISR). However, these methods tend to generate inaccurate …
Image Super-ResolutionSuper-ResolutionLocalizing and Mitigating Memorization in Image Autoregressive Models
Image AutoRegressive (IAR) models have achieved state-of-the-art performance in speed and quality of generated images. However, they also raise concerns about memorization of their training data and its implications for …
AlignVAR: Towards Globally Consistent Visual Autoregression for Image Super-Resolution
Visual autoregressive (VAR) models have recently emerged as a promising alternative for image generation, offering stable training, non-iterative inference, and high-fidelity synthesis through next-scale prediction. This…
Image Super-ResolutionImage GenerationFlexVAR: Flexible Visual Autoregressive Modeling without Residual Prediction
This work challenges the residual prediction paradigm in visual autoregressive modeling and presents FlexVAR, a new Flexible Visual AutoRegressive image generation paradigm. FlexVAR facilitates autoregressive learning wi…
Image GenerationPrediction